CSV

dataframe = healthcareai.load_csv('path_to_your/data.csv')

Step 2: Set up a Trainer

The SupervisedModelTrainer class helps you train models. It cleans and prepares the data before model creation. It also assignes parameters specific to the type of model you eventually want (regression or classification). To set up a trainer you'll need these arguments:

dataframe(pandas.core.frame.DataFrame): The training data in a pandas dataframe

predicted_column(str): The name of the prediction column

model_type(str): the trainer type - 'classification' or 'regression'

impute(bool): True to impute data (mean of numeric columns and mode of categorical ones). False to drop rows
that contain any null values.

Step 4: Evaluate and compare models

Now that you have trained some models, let's evaluate and compare them.

Each trained model has metrics that can be easily viewed by using the .metrics property. Depending on the model type, this can be a large list, so if you just want to see the ROC or PR metrics you can use .roc() or .pr() methods to print out the ideal cutoff and full list of cutoffs.